shuttleai/shuttle-2.5-mini
Shuttle-2.5-mini is a 13 billion parameter multilingual language model developed by ShuttleAI Inc., fine-tuned from Mistral-Nemo-Base-2407. It is specifically designed to excel in complex chat, reasoning, and agent tasks, with a unique focus on emulating the writing style of Claude 3 models and extensive training on role-playing data. This model supports a 128k context window and is pretrained on a large proportion of multilingual and code data, making it suitable for diverse communication and specialized interactive applications.
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Shuttle-2.5-mini Overview
Shuttle-2.5-mini is a 13 billion parameter multilingual language model developed by ShuttleAI Inc., released under the Apache 2.0 License. It is a fine-tuned version of the Mistral-Nemo-Base-2407 model, specifically engineered to excel in complex chat, reasoning, and agent tasks. A key differentiator is its extensive training to emulate the writing style of Claude 3 models and its thorough fine-tuning on role-playing data, making it highly adept at nuanced and interactive conversational scenarios.
Key Capabilities
- Claude 3 Style Emulation: Fine-tuned to mimic the prose quality and conversational style of Claude 3 models.
- Role-Playing Proficiency: Extensively trained on role-playing data for highly engaging and context-aware interactions.
- Multilingual Support: Pretrained on a significant proportion of multilingual data, supporting diverse language applications.
- Extended Context Window: Features a 128k context window, allowing for processing longer and more complex inputs.
- Reasoning and Agent Tasks: Designed to perform well in complex reasoning and agent-based applications.
Good for
- Advanced Chatbots: Ideal for creating chatbots that require sophisticated conversational styles and role-playing abilities.
- Multilingual Applications: Suitable for applications needing robust performance across various languages.
- Interactive Storytelling & Gaming: Excels in scenarios requiring dynamic character interactions and narrative generation.
- Agent-Based Systems: Useful for developing AI agents that need to understand and respond within specific contexts and personas.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.